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gcnconv.py 5.82 KB
fengxun authored 2023-08-21 09:07 . 1
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""GCNConv Layer"""
import mindspore as ms
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.initializer import XavierUniform
from mindspore.nn.cell import Cell
from mindspore_gl import Graph
from .. import GNNCell
class GCNConv(GNNCell):
r"""
Graph Convolution Network Layer.
From the paper Semi-Supervised Classification with Graph Convolutional Networks
<https://arxiv.org/abs/1609.02907>_ .
.. math::
h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ji}}h_j^{(l)}W^{(l)})
:math:\mathcal{N}(i) represents the neighbour node of :math:i.
:math:c_{ji} = \sqrt{|\mathcal{N}(j)|}\sqrt{|\mathcal{N}(i)|}.
.. math::
h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{e_{ji}}{c_{ji}}h_j^{(l)}W^{(l)})
Args:
in_feat_size (int): Input node feature size.
out_size (int): Output node feature size.
activation (Cell, optional): Activation function. Default: None.
dropout (float, optional): The dropout rate, greater than 0 and less equal than 1. E.g. dropout=0.1,
dropping out 10% of input units. Default: 0.5.
Inputs:
- **x** (Tensor) - The input node features. The shape is :math:(N, D_{in})
where :math:N is the number of nodes,
and :math:D_{in} should be equal to in_feat_size in Args.
- **in_deg** (Tensor) - In degree for nodes. The shape is :math:(N, ) where :math:N is the number of nodes.
- **out_deg** (Tensor) - Out degree for nodes. The shape is :math:(N, )
where :math:N is the number of nodes.
- **g** (Graph) - The input graph.
Outputs:
- Tensor, output node features with shape of :math:(N, D_{out}), where :math:(D_{out}) should be the same as
out_size in Args.
Raises:
TypeError: If in_feat_size or out_size is not an int.
TypeError: If dropout is not a float.
TypeError: If activation is not a mindspore.nn.Cell.
ValueError: If dropout is not in range (0.0, 1.0]
Supported Platforms:
Ascend GPU
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import GCNConv
>>> from mindspore_gl import GraphField
>>> n_nodes = 4
>>> n_edges = 7
>>> feat_size = 4
>>> src_idx = ms.Tensor([0, 1, 1, 2, 2, 3, 3], ms.int32)
>>> dst_idx = ms.Tensor([0, 0, 2, 1, 3, 0, 1], ms.int32)
>>> ones = ms.ops.Ones()
>>> feat = ones((n_nodes, feat_size), ms.float32)
>>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
>>> in_degree = ms.Tensor([3, 2, 1, 1], ms.int32)
>>> out_degree = ms.Tensor([1, 2, 1, 2], ms.int32)
>>> gcnconv = GCNConv(in_feat_size=4, out_size=2, activation=None, dropout=1.0)
>>> res = gcnconv(feat, in_degree, out_degree, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)
"""
def __init__(self,
in_feat_size: int,
out_size: int,
activation=None,
dropout=0.5):
super().__init__()
if in_feat_size <= 0 or not isinstance(in_feat_size, int):
raise ValueError("in_feat_size must be positive int")
if out_size <= 0 or not isinstance(out_size, int):
raise ValueError("out_size must be positive int")
if not isinstance(dropout, float):
raise ValueError("dropout must be float")
self.in_feat_size = in_feat_size
self.out_size = out_size
if dropout < 0.0 or dropout >= 1.0:
raise ValueError(f"For '{self.cls_name}', the 'dropout_prob' should be a number in range [0.0, 1.0), "
f"but got {dropout}.")
if activation is not None and not isinstance(activation, Cell):
raise TypeError(f"For '{self.cls_name}', the 'activation' must a mindspore.nn.Cell, but got "
f"{type(activation).__name__}.")
self.fc = ms.nn.Dense(in_feat_size, out_size, weight_init=XavierUniform(), has_bias=False)
self.bias = ms.Parameter(initializer('zero', (out_size), ms.float32), name="bias")
self.activation = activation
self.min_clip = Tensor(1, ms.int32)
self.max_clip = Tensor(100000000, ms.int32)
self.drop_out = ms.nn.Dropout(p=dropout)
# pylint: disable=arguments-differ
def construct(self, x, in_deg, out_deg, g: Graph):
"""
Construct function for GCNConv.
"""
out_deg = ms.ops.clip_by_value(out_deg, self.min_clip, self.max_clip)
out_deg = ms.ops.Reshape()(ms.ops.Pow()(out_deg, -0.5), ms.ops.Shape()(out_deg) + (1,))
x = self.drop_out(x)
x = ms.ops.Squeeze()(x)
x = x * out_deg
x = self.fc(x)
g.set_vertex_attr({"x": x})
for v in g.dst_vertex:
v.x = g.sum([u.x for u in v.innbs])
in_deg = ms.ops.clip_by_value(in_deg, self.min_clip, self.max_clip)
in_deg = ms.ops.Reshape()(ms.ops.Pow()(in_deg, -0.5), ms.ops.Shape()(in_deg) + (1,))
x = [v.x for v in g.dst_vertex] * in_deg
x = x + self.bias
return x

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